Can Tumor Necrosis Factor Inhibitors Be Safely Used in Pregnancy?
Bibliographic record
Abstract
OBJECTIVE: We review available safety data for use of currently approved tumor necrosis factor (TNF) inhibitors during pregnancy and lactation and suggest guidelines for use of these agents among women of reproductive age. Method. Although regulatory agencies encourage the inclusion of pregnant women and those of child-bearing age in randomized controlled trials, pregnant and lactating women have universally been excluded from studies because of unknown or potential risks to the fetus. Thus, strong evidence-based treatment recommendations during pregnancy are usually lacking and safety information is derived from voluntary reports of adverse events during postmarketing surveillance or via uncontrolled, observational studies, reviewed here. RESULTS: Uncommon adverse pregnancy outcomes observed with TNF inhibitor therapy appear to approximate those seen in women not receiving such therapy and may include premature birth, miscarriage, low birthweight, hypertension, and preeclampsia. There are rare reports of fetal malformations or congenital anomalies in patients exposed to TNF inhibitors during conception or pregnancy. However, the incidence of these events appears to be far below the 3% rate of congenital anomalies in the general population. CONCLUSION: If the activity or disease severity precludes the cessation of a TNF inhibitor and/or DMARD, uncontrolled observations suggest that conception and early pregnancy are not adversely affected by use of TNF inhibitors. Nearly 70% of pregnant patients can discontinue their TNF inhibitor early in the pregnancy (or with determination of pregnancy) without augmenting maternal or fetal risks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".